Soft information finds use in a variety of “belief propagation” systems, such as in communications signal decoding where, as a simple example, a series of soft decisions indicate whether corresponding bits in a received communication signal are more likely 1s or 0s. “Turbo” type receivers are a particularly well-known and useful example of soft information processing. Numerous technical references are available regarding the fundamental aspects of Turbo coding and decoding. See, for example, C. Berrou, A. Glavieux, et al., “Near Shannon limit error-correcting coding and decoding: Turbo-codes”, Proceedings of ICC 1993, Geneva, Switzerland, pp. 1064-1070; and C. Douillard, A. Picart, P. Didier, M. Jézéquel, C. Berrou, and A. Glavieux, “Iterative correction of intersymbol interference: turbo-equalization”, European Transactions on Telecommunications, Vol. 6, No. 5, September-October 1995, pp. 507-512.
During the last two decades, the Turbo principle has been used in constructing a myriad of channel coding schemes, as well as iterative receiver approaches for applications beyond channel coding. However, in a basic Turbo decoding approach, the target receiver implements an iterative decoding structure in which two relatively simple constituent decoders exchange soft information, so that the probability estimates (“beliefs”) from one decoder aid the other decoder in refining its estimates. The iterative exchange of probability values—such as Log Likelihood Ratios or LLRs—allows Turbo-based coding and decoding schemes to achieve near Shannon limit performance, despite the use of relatively simple constituent codes.
In more detail, all Turbo receiver algorithms are based on the principle of belief propagation. A number of “decoding” stages provide output soft information about some part of the received signal that is an improved, value-added version of the input soft information, accounting for the local “constituent code” structure and any extrinsic information available from other decoders in the Turbo structure.
The “constituent code” and the corresponding “decoder” may also refer, for example, to a multipath propagation channel and a corresponding equalizer, or to a multiple-access channel and a corresponding Interference Cancellation (“IC”) operation. Various examples of Turbo structures beyond channel coding are the Turbo equalizer, the Turbo-IC with soft TDEC (“Turbo Decoder”) for MIMO (“Multiple-Input-Multiple-Output”) reception. A typical Turbo-IC receiver employs iterative soft IC methods to treat a mix of signal components and approaches the performance of joint detection/decoding for the relevant signal components.
The belief propagation process is realized by exchanging soft bit information between the constituent decoders, or more generally, between the different parts of the Turbo receiver. The soft information may be in the form of an absolute bit probability that a given bit is 0 or 1, or as extrinsic information, i.e., the “added value” provided by the decoder over the input soft information. The optimal information type to be transferred depends on the insertion point in the receiver structure and other design choices.
The linear bit probabilities or extrinsic values fall in the range [0 . . . 1]. In practical implementations, LLRs are used instead, because of their convenient additive property. The LLR values may assume any real value. Small magnitudes indicate uncertain bits, or little added value from the recent decoding iteration in the case of extrinsic information.
While the advantages of Turbo receivers are well understood as a general proposition, their implementation can be challenging, particularly in high data rate environments. In high-rate systems, large blocks of information bits need to be decoded from a received communication signal at each Transmission Time Interval or “TTI.” In Release 10 (“Rel-10”) of the Long Term Evolution (“LTE”) specifications, as promulgated by the Third Generation Partnership Project (“3GPP”), the peak Downlink (“DL”) data rate is up to 3 Gb/s for an 8-layer transmission supported by a category-8 UE (“User Equipment”). For such a top-category UE, even one using a “simple” receiver, the peak data rate implies a large demodulation and Turbo decoding load, including the need for an efficient memory management system to retrieve, transfer, and store a large number of soft values in the Turbo processing chain.
For the scenario above, at each TTI, soft values for 3.6E7 (almost 40 million) coded bits are extracted in the demodulator, transferred to the TDEC, and the soft values for 3E6 information bits are iterated a number of times in the TDEC before delivering them to the higher protocol or network layers. This amount of data and processing imposes a considerable and possibly impractical load on the memory sub-systems in a UE. More generally, this degree of loading imposes significant processing power requirements, which are in direct tension with cost, power consumption, and size constraints.
Further exacerbating the design challenges, more advanced UEs may have need for even more elaborate receiver structures. For example, an advanced receiver may perform N iterations of the Turbo-IC loop to detect parallel MIMO streams. In slightly simplified terms, the total memory bandwidth (“BW”) required by such an architecture is N times higher than the baseline requirements identified above. In a practical example, the value of N may be four, i.e., N=4, and the processing load/BW multiplier is therefore substantial. These circumstances leave open and unaddressed the challenge of developing suitable, cost-effective circuitry for implementing Turbo receivers and other types of soft-value processing apparatuses.